Team Vav Project Implementation

The task for the Kaggle DataScience Bowl, 2018 is to create an algorithm to automate nucleus detection. This project has been our teams attempt to come up with a solution for this problem.

More details can be found here.

Getting Started

If you follow the below instructions it will allow you to install and run the training or testing.

Prerequisites

What things you need to install the software and how to install them

• Python 2.7
• Anaconda - Python Environment virtualization.
• Keras Deep Learning API for Tensorflow.
• Tensorflow Deep Learning Library.

Installing

Anaconda

Anaconda is a complete Python distribution embarking automatically the most common packages, and allowing an easy installation of new packages.

Download and install Anaconda from (https://www.continuum.io/downloads). The link for Linux,Mac and Windows are in the website.Following their instruction will install the tool.

Running Environment

• Once Anaconda is installed open anaconda prompt(Windows/PC) Command Line shell(Mac OSX or Unix)
• Run conda env create -f environment.yml will install all packages required for all programs in this repository

To start the environment

• For Unix like systems source activate vav

• For PC like systems activate vav

Keras

You can install keras using pip on command line sudo pip install keras

The environment.yml file for conda is placed in Extra for your ease of installation this has keras

Tensorflow

Installing Tensorflow is straight forward using pip on command line

• If CPU then sudo pip install tensorflow
• If GPU then sudo pip install tensorflow-gpu

The environment.yml file for conda is placed in Extra for your ease of installation this has tensorflow.

Downloading the dataset (Optional)

Refer to downloading the dataset on this page: Data

Data

This dataset contains a large number of segmented nuclei images. The images were acquired under a variety of conditions and vary in the cell type, magnification, and imaging modality (brightfield vs. fluorescence). The dataset is designed to challenge an algorithm's ability to generalize across these variations. For more description on the dataset please refer [here] (https://www.kaggle.com/c/data-science-bowl-2018/data).

Running and Training

One can run unet.py via regular python

$ python unet.py [train or Test] [optional args]

Example: python unet.py train

• Required Arguments

  • trainortest: This is a string either train or test

• Optional Arguments

  • -batch-size: The batch size if applicable (Default: 20)
  • -masks: Path to the masks directory where masks are present. (Default: train\masks)
  • -dataset: Path to the dataset directory where train dataset is present. (Default: train\)

Results

Method	Mean IoU Score (on Kaggle Board)
Threshold UNet [1]	0.278
Thresholding + Median Filter UNet [4]	0.335

Authors

• Ankita Joshi - AnkitaJo
• Vibhod Phenani - vibodh01
• Vyom Srivastava - vyom1911

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments and References

• Hat tip to anyone who's code was used